Temperature sensitivities of vegetation indices and aboveground biomass are primarily linked with warming magnitude in high-cold grasslands

Assessing the impact of climate warming on tree species composition and distribution in the forest region of Northeast China

Global climate change has markedly influenced the structure and distribution of mid-high-latitude forests. In the forest region of Northeast China, the magnitude of climate warming surpasses the global average, which presents immense challenges to the survival and habitat sustainability of dominant tree species. We predicted the potential changes in aboveground biomass, dominant tree species composition, and distribution in the forest region of Northeast China over the next century under different climatic conditions encompassing the current scenario and future scenarios (RCP2.6, RCP4.5, and RCP8.5). Forest ecosystem process model LINKAGES 3.0 was used to simulate dynamic changes in species-level aboveground biomass under four climate scenarios at the homogeneous land-type unit level. The potential spatial distribution of tree species was investigated based on three indicators: extinction, colonization, and persistence. The results showed that LINKAGES 3.0 model effectively simulated the aboveground biomass of 17 dominant tree species in the forest region of Northeast China, achieving a high accuracy with R² = 0.88. Under the current, RCP2.6, and RCP4.5 climate scenarios, the dominant tree species presented gradual increases in aboveground biomass, whereas under RCP8.5, an initial increase and subsequent decline were observed. With increasing warming magnitude, cold-temperate coniferous tree species will gradually be replaced by other temperate broad-leaved tree species. Furthermore, a large temperature increase under RCP8.5 will likely produce a significant contraction in the potential distribution range of tree species like Larch, Scotch pine, Ribbed birch, Spruce and Fir, while most temperate broad-leaved tree species and Korean pine are expected to demonstrate a northward migration. These findings provide guidance for enhancing the adaptability and resilience of forest ecosystems in middle and high latitudes and addressing the threats posed by climate warming.

Incorporating high-resolution climate, remote sensing and topographic data to map annual forest growth in central and eastern Europe

To enhance our understanding of forest carbon sequestration, climate change mitigation and drought impact on forest ecosystems, the availability of high-resolution annual forest growth maps based on tree-ring width (TRW) would provide a significant advancement to the field. Site-specific characteristics, which can be approximated by high-resolution Earth observation by satellites (EOS), emerge as crucial drivers of forest growth, influencing how climate translates into tree growth. EOS provides information on surface reflectance related to forest characteristics and thus can potentially improve the accuracy of forest growth models based on TRW. Through the modelling of TRW using EOS, climate and topography data, we showed that species-specific models can explain up to 52 % of model variance (Quercus petraea), while combining different species results in relatively poor model performance (R2 = 13 %). The integration of EOS into models based solely on climate and elevation data improved the explained variance by 6 % on average. Leveraging these insights, we successfully generated a map of annual TRW for the year 2021. We employed the area of applicability (AOA) approach to delineate the range in which our models are deemed valid. The calculated AOA for the established forest-type models was 73 % of the study region, indicating robust spatial applicability. Notably, unreliable predictions predominantly occurred in the climate margins of our dataset. In conclusion, our large-scale assessment underscores the efficacy of combining climate, EOS and topographic data to develop robust models for mapping annual TRW. This research not only fills a critical void in the current understanding of forest growth dynamics but also highlights the potential of integrated data sources for comprehensive ecosystem assessments.

Biomod2 modeling for predicting the potential ecological distribution of three Fritillaria species under climate change

The Fritillaria species ranked as a well-known traditional medicine in China and has become rare due to excessive harvesting. To find reasonable strategy for conservation and cultivation, identification of new ecological distribution of Fritillaria species together with prediction of those responses to climate change are necessary. In terms of current occurrence records and bioclimatic variables, the suitable habitats for Fritillaria delavayi, Fritillaria taipaiensis, and Fritillaria wabuensis were predicted. In comparison with Maxent and GARP, Biomod2 obtained the best AUC, KAPPA and TSS values of larger than 0.926 and was chosen to construct model. Temperature seasonality was indicated to put the greatest influence on Fritillaria taipaiensis and Fritillaria wabuensis, while isothermality was of most importance for Fritillaria delavayi. The current suitable areas for three Fritillaria species were distributed in south-west China, accounting for approximately 17.72%, 23.06% and 20.60% of China's total area, respectively. During 2021-2100 period, the suitable habitats of F. delavayi and F. wabuensis reached the maximum under SSP585 scenario, while that of F. taipaiensis reached the maximum under SSP126 scenario. The high niche overlap among three Fritillaria species showed correlation with the chemical composition (P ≤ 0.05), while no correlation was observed between niche overlap and DNA barcodes, indicating that spatial distribution had a major influence on chemical composition in the Fritillaria species. Finally, the acquisition of species-specific habitats would contribute to decrease in habitat competition, and future conservation and cultivation of Fritillaria species.

Considering Climatic Factors, Time Lag, and Cumulative Effects of Climate Change and Human Activities on Vegetation NDVI in Yinshanbeilu, China

Climate and human activities are the basic driving forces that control and influence the spatial distribution and change of vegetation. Using trend analysis, the Hurst index, correlation analysis, the Moran index, path analysis, residual analysis, and other methods, the effects of human activities and climate factors on vegetation change were analyzed. The results show that: (1) The research area's normalized difference vegetation index (NDVI) exhibited a substantial upward trend from 2001 to 2020, increasing at a rate of 0.003/a, and the vegetation cover was generally healthy. The generally constant NDVI region made up 78.45% of the entire area, and the grassland, cultivated land, and forest land showed the most visible NDVI aggregation features. (2) The Vegetation is mainly promoted by water and heat, particularly precipitation, have a major impact on plants, with the direct influence of precipitation on vegetation growth being much greater than the indirect effect through the temperature. (3) The trend of NDVI residuals showed obvious spatial variability, presenting a distribution characteristic of high in the south and low in the north. The results of this study can provide a basis for the scientific layout of ecological protection and restoration projects in the Yinshanbeilu area.

Impacts of Anthropogenic Activities and Climate Change on Forage Nutrition Storage in Tibetan Grasslands

Uncertainties about the impacts of anthropogenic activities and climate change on forage nutrition storage of grasslands can limit the adaptive management of grasslands across the whole Tibetan Plateau. The main objective was to investigate the impacts of anthropogenic activities and climate change on the forage nutrition storage of grasslands on the Tibetan Plateau. Based on random forest models, we quantified the responses of forage nutrition storage to anthropogenic activities and climate change across the whole Tibetan grasslands from 2000 to 2020. Warming and increased precipitation did not always increase forage nutrition storage, and cooling and decreased precipitation did not always reduce forage nutrition storage. Compared to temperature and precipitation changes, radiation change had stronger contributions to potential and actual forage nutrition storage. Humankind's activities altered the impacts of climate change on forage nutrition storage. The impacts of anthropogenic activities on forage nutrition storage increased linearly with increasing mean annual temperature and decreasing elevation but showed quadratic relationships with longitude, mean annual precipitation and radiation. The change in the impacts of humankind's activities on forage nutrition storage was more closely related to radiation change than temperature and precipitation changes. The findings observed by this study caution that the impacts of radiation change on forage nutrition forage should be taken seriously under global change. Both climate change and humankind activities cannot always increase forage nutrition storage but may cause the degradation of forage nutrition storage.

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.

Non-growing/growing season non-uniform-warming increases precipitation use efficiency but reduces its temporal stability in an alpine meadow

There are still uncertainties on the impacts of season-non-uniform-warming on plant precipitation use efficiency (PUE) and its temporal stability (PUE_stability) in alpine areas. Here, we examined the changes of PUE and PUE_stability under two scenes of non-growing/growing season non-uniform-warming (i.e., GLNG: growing-season-warming lower than non-growing-season-warming; GHNG: growing-season-warming higher than non-growing-season-warming) based on a five-year non-uniform-warming of non-growing/growing season experiment. The GLNG treatment increased PUE by 38.70% and reduced PUE_stability by 50.47%, but the GHNG treatment did not change PUE and PUE_stability. This finding was mainly due to the fact that the GLNG treatment had stronger influences on aboveground biomass (AGB), non-growing-season soil moisture (SM_NG), temporal stability of AGB (AGB_stability), temporal stability of non-growing-season air temperature (T _{a_NG_stability}), temporal stability of growing-season vapor pressure deficit (VPD_{G_stability}) and temporal stability of start of growing-season (SGS_stability). Therefore, the warming scene with a higher non-growing-season-warming can have greater influences on PUE and PUE_stability than the warming scene with a higher growing-season-warming, and there were possibly trade-offs between plant PUE and PUE_stability under season-non-uniform-warming scenes in the alpine meadow.

Leaf traits divergence and correlations of woody plants among the three plant functional types on the eastern Qinghai-Tibetan Plateau, China

Leaf traits are important indicators of plant life history and may vary according to plant functional type (PFT) and environmental conditions. In this study, we sampled woody plants from three PFTs (e.g., needle-leaved evergreens, NE; broad-leaved evergreens, BE; broad-leaved deciduous, BD) on the eastern Qinghai-Tibetan Plateau, and 110 species were collected across 50 sites. Here, the divergence and correlations of leaf traits in three PFTs and relationships between leaf traits and environment were studied. The results showed significant differences in leaf traits among three PFTs, with NE plants showed higher values than BE plants and BD plants for leaf thickness (LT), leaf dry matter content (LDMC), leaf dry mass per area (LMA), carbon: nitrogen ratio (C/N), and nitrogen content per unit area (N_area), except for nitrogen content per unit mass (N_mass). Although the correlations between leaf traits were similar across three PFTs, NE plants differed from BE plants and BD plants in the relationship between C/N and N_area. Compared with the mean annual precipitation (MAP), the mean annual temperature (MAT) was the main environmental factor that caused the difference in leaf traits among three PFTs. NE plants had a more conservative approach to survival compared to BE plants and BD plants. This study shed light on the regional-scale variation in leaf traits and the relationships among leaf traits, PFT, and environment. These findings have important implications for the development of regional-scale dynamic vegetation models and for understanding how plants respond and adapt to environmental change.

Geo-Distribution Patterns of Soil Fungal Community of Pennisetum flaccidum in Tibet

Pennisetum flaccidum can be used as a pioneer species for the restoration of degraded grasslands and as a high-quality forage for local yak and sheep in alpine regions. The geographical distribution pattern of soil fungal community can modify that of P. flaccidum. A field survey along 32 sampling sites was conducted to explore the geo-distribution patterns of soil fungal community of P. flaccidum in Tibet. Soil fungal species, phylogenetic and function diversity generally had a closer correlation with longitude/elevation than latitude. The geo-distribution patterns of soil fungal species, phylogenetic and function diversity varied with soil depth. Soil fungal species, phylogenetic and function diversity had dissimilar geo-distribution patterns. Precipitation had stronger impacts on total abundance, species α-diversity, phylogenetic α-diversity, and function β-diversity than temperature for both topsoil (0-10 cm depth) and subtopsoil (10-20 cm depth). Furthermore, precipitation had stronger impacts on function α-diversity for topsoil, species β-diversity for topsoil, and phylogenetic β-diversity for subtopsoil than temperature. The combination of species, phylogenetic and function diversity can better reflect geo-distribution patterns of soil fungal community. Compared to global warming, the impact of precipitation change on the variation in soil fungal community of P. flaccidum should be given more attention.

Response of forage nutrient storages to grazing in alpine grasslands

Forage nutrient storages can determine livestock size and husbandry development. There is insufficient research on the response of forage nutrient storages to grazing and related driving mechanisms in alpine grasslands, especially on the Tibetan Plateau. This study conducted a grazing experiment in three alpine grassland sites along an elevation gradient (two warm-season pastures and one cold-season pasture; two alpine steppe meadow sites and one alpine meadow) of Northern Tibet. Different types of alpine grassland ecosystems, at least for forage nutrient storages, may have different responses to grazing. Warm-season grazing significantly reduced crude protein (CP) storage, acid detergent fiber (ADF) storage, and neutral detergent fiber (NDF) storage of high-quality forage by 53.29, 63.82, and 63.26%, respectively, but cold-season grazing did not significantly alter the CP, ADF and NDF storages of high-quality forage. Warm-season grazing significantly reduced CP, ADF, NDF, crude ash (Ash), ether extract (EE) and water-soluble carbohydrate (WSC) storages of the plant community by 46.61, 62.47, 55.96, 64.94, 60.34, and 52.68%, and forbs by 62.33, 77.50, 73.69, 65.05, 57.75, and 62.44% in the alpine meadow site but not the alpine steppe meadow site, respectively. Plant species and phylogenetic diversity had different relationships with forage nutrient storages. The elevation distribution of forage nutrient storages under fencing conditions were different from those under grazing conditions. Therefore, cold-season grazing can have lower negative effects on forage nutrient storages than warm-season grazing. Combined plant species with phylogenetic diversity and composition can be better in predicting forage nutrient storages. Grazing can restructure the elevation distribution of forage nutrient storages in alpine grasslands.

Response of forage nutritional quality to climate change and human activities in alpine grasslands

The impacts of climate change and human activities on forage nutritional quality will affect nutrient capacity, livestock development and wildlife conservation in alpine regions. However, the response of forage nutritional quality to climate change and human activities remains indistinguishable across the whole Tibet. Here, six forage variables (i.e., crude protein, CP; ether extract, EE; crude ash, Ash; acid detergent fiber, ADF; neutral detergent fiber, NDF; water-soluble carbohydrates, WSC) together represented forage nutritional quality. We estimated potential forage CP, EE, Ash, ADF, NDF and WSC contents using growing mean air temperature, total precipitation and total radiation based on random forest models. We also estimated actual forage CP, EE, Ash, ADF, NDF and WSC contents using growing mean air temperature, total precipitation and total radiation, and maximum normalized difference vegetation index based on random forest models. Climate change had nonlinear effects on potential forage CP, EE, Ash, ADF, NDF and WSC contents. Radiation change predominated the variations of potential forage nutritional quality. Human activities altered the sensitivities of forage nutritional quality to climate change. The effects of human activities on forage nutritional quality increased with increasing longitude and precipitation, and decreasing elevation and radiation. Consequently, we should pay attention to the radiation change besides climate warming and precipitation change, at least for forage nutritional quality in alpine grasslands. The effects of human activities on forage nutritional quality can vary with longitude, elevation, precipitation and radiation in alpine grasslands.

Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands

Quantifying forage nutritional quality and pool at various spatial and temporal scales are major challenges in quantifying global nitrogen and phosphorus cycles, and the carrying capacity of grasslands. In this study, we modeled forage nutrition quality and storage using climate data under fencing conditions, and using climate data and a growing-season maximum normalized-difference vegetation index under grazing conditions based on four different methods (i.e., multiple linear regression, random-forest models, support-vector machines and recursive-regression trees) in the alpine grasslands of Tibet. Our results implied that random-forest models can have greater potential ability in modeling forage nutrition quality and storage than the other three methods. The relative biases between simulated nutritional quality using random-forest models and the observed nutritional quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were lower than 2.00% and 6.00%, respectively. The RMSE between simulated nutrition quality using random-forest models and the observed nutrition quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were no more than 0.99% and 4.50 g m−2, respectively. Therefore, random-forest models based on climate data and/or the normalized-difference vegetation index can be used to model forage nutrition quality and storage in the alpine grasslands of Tibet.